A Sequence-to-Sequence Text Summarization Using Long Short-Term Memory Based Neural Approach
International journal of intelligent engineering and systems · 2023
The massive development in the different fields of engineering and science brought a significant change in the modern years.In the field of natural language processing, automatic text summarization is known as one of the important research directions.The text's primary concepts and flow should be considered to provide a solid summary that limits the repetition in the text as a summary.A sentence is referred to as a basic language unit and there are many semantic links present among the prison terms like example-of, cause-effect, sequential, etc. within a meaningful text.In this research manuscript, an automated text summarization model is developed and its performance is validated on the two challenging datasets such as daily-mail and Gigaword.In this manuscript, a transformer: long short term memory (LSTM) neural network is used along with the Huber loss function and Adam optimizer.The transformer used in this research (LSTM with Huber loss function and Adam optimizer) includes the advantages like requiring limited memory, computationally effective, and easy implementation.The proposed neural network mainly aims in exhibiting the summaries that are composed by the groups or paragraphs that includes more keywords or phrases than summaries composed by sentences.The non-differential evaluation metrics are utilized in the proposed sequence-tosequence model to provide semantic information of input text and it stores important features for effective text summarization.The size of the parameters is specified by the maximum number of sentences in the same group.The proposed LSTM with Huber loss function and Adam optimizer has 44.51 ROUGE-1, 20.43 ROUGE-2, and 40.08 ROUGE-L on dailymail dataset.Experimental results of the trial demonstrated that the proposed model outperformed the existing models like dynamic residual network, convolutional neural network with LSTM, and knowledge powered topic level attention model in terms of the rouge parameter.